I don’t know why, but i dont get the visualisations on the GitHub page. Also, the data set COMPMUS isnt available for my computer. The assignment for me this week makes this impossible since i dont can work with the compmus dataset.
I want to know whether choirs who name themselves popchoirs are really popchoirs, that is, whether their repertoire mainly consists of songs in the genre pop. I will choose 10 different choirs and will analyse their repertoire. These can be found on their websites.
First, I chose the following 10 choirs:
Second, I made playlists for all the above mentioned choirs.
Third, I will compare each of these playlists with a playlist I make on my own -> Popmusic in Holland from 1965-2020.
Before I can compare the playlists which each other I need a definition of what popmusic in Holland is.
What I need to do for now/this week; https://www.top40.nl/bijzondere-lijsten/top-100-jaaroverzichten/1965 This website lists the 100 most popular songs in Holland of each year since 1965. Since 55 years with 100 songs is rather a lot of work I will focus on the first 40 songs of each year. This will be a total amount of songs of 2200. From here I can make the make the definition of what we could call in Holland popular music. After that I can start comparing all the popchoir playlists with the playlist I made for ‘defining pop’
This is a second column of introductory text.
To define what could be called popmusic in Holland I took 55 playlists from the Top100 in Holland from 1965-2020. Each playlist consists approximately 100 songs from that specific year. With rbind() I combined all the songs together. I removed the songs that were doubled in the playlists. So from 55 years the Top 100 songs the data is 5044 songs. This means I have 5044 songs to define Popmusic.
To specify more the components we will discuss the characteristics of popmusic with the features of spotify:
Hier tabellen van de gemiddelde van alles -> een tabel of meerdere?
Hier de popkoren die ik ga vergelijken
uitproberen of de tabel van Ahsley wel werkt.
award_labels <-
tibble(
label = c("Top 100 alles", "Top 100 1965"),
playlist = c("1965-2019", "1965"),
valence = c(0.153, 0.828),
energy = c(0.119, 0.717),
)
awards %>% # Start with awards.
mutate(
mode = ifelse(mode == 0, 'Minor', 'Major')
) %>%
ggplot( # Set up the plot.
aes(
x = valence,
y = energy,
size = loudness,
colour = mode
)
) +
geom_point() + # Scatter plot.
geom_rug(size = 0.1) + # Add 'fringes' to show data distribution.
geom_text( # Add text labels from above.
aes(
x = valence,
y = energy,
label = label),
colour = "black", # Override colour (not mode here).
size = 3, # Override size (not loudness here).
data = award_labels, # Specify the data source for labels.
hjust = "left", # Align left side of label with the point.
vjust = "bottom", # Align bottom of label with the point.
nudge_x = -0.05, # Nudge the label slightly left.
nudge_y = 0.02 # Nudge the label slightly up.
) +
facet_wrap(~ playlist) + # Separate charts per playlist.
scale_x_continuous( # Fine-tune the x axis.
limits = c(0, 1),
breaks = c(0, 0.50, 1), # Use grid-lines for quadrants only.
minor_breaks = NULL # Remove 'minor' grid-lines.
) +
scale_y_continuous( # Fine-tune the y axis in the same way.
limits = c(0, 1),
breaks = c(0, 0.50, 1),
minor_breaks = NULL
) +
scale_colour_brewer( # Use the Color Brewer to choose a palette.
type = "qual", # Qualitative set.
palette = "Paired" # Name of the palette is 'Paired'.
) +
scale_size_continuous( # Fine-tune the sizes of each point.
trans = "exp", # Use an exp transformation to emphasise loud.
guide = "none" # Remove the legend for size.
) +
theme_light() + # Use a simpler them.
labs( # Make the titles nice.
x = "Valence",
y = "Energy",
colour = "Mode"
)hier de conclusie